Survey on Human Behavior Recognition Using Affective Computing

Jadhav, Narendra; Sugandhi, Rekha

doi:10.1109/gcwcn.2018.8668632

“…Meanwhile, most deep learning-based methods have been proposed during the last three years [16]- [22], which mentions that both supervised and unsupervised deep learning methodologies could solve the recognition tasks according to various data formats and labeling methods. The above timeline [23]- [43] illustrates the methodology revolution process from 2018. The timeline says that more combinations of methodologies are developed rather than individual model usage.…”

Section: B Timeline Of Related Researchesmentioning

confidence: 99%

Deep Neural Networks in Video Human Action Recognition: A review

Wang¹,

Zheng²,

yang³

et al. 2023

Preprint

View full text Add to dashboard Cite

<p>Currently, spatial-temporal behavior recognition is one of the most foundational tasks of computer vision. The 2D neural networks of deep learning are built for recognizing pixel-level information such as images with RGB, RGB-D, or optical flow formats, with the current increasingly wide usage of surveillance video and more tasks related to human action recognition. There are increasing tasks requiring temporal information for frames dependency analysis. The researchers have widely studied video-based recognition rather than image-based(pixel-based) only to extract more informative elements from geometry tasks. Our current related research addresses multiple novels proposed research works and compares their advantages and disadvantages between the derived deep learning frameworks rather than machine learning frameworks. The comparison happened between existing frameworks and datasets, which are video format data only. Due to the specific properties of human actions and the increasingly wide usage of deep neural networks, we collected all research works within the last three years between 2020 to 2022. In our article, the performance of deep neural networks surpassed most of the techniques in the feature learning and extraction tasks, especially video action recognition.</p>

show abstract

“…Meanwhile, most deep learning-based methods have been proposed during the last three years [16]- [22], which mentions that both supervised and unsupervised deep learning methodologies could solve the recognition tasks according to various data formats and labeling methods. The above timeline [23]- [43] illustrates the methodology revolution process from 2018. The timeline says that more combinations of methodologies are developed rather than individual model usage.…”

Section: B Timeline Of Related Researchesmentioning

confidence: 99%

Deep Neural Networks in Video Human Action Recognition: A review

Wang¹,

Zheng²,

yang³

et al. 2023

Preprint

2

0

View full text Add to dashboard Cite

<p>Currently, spatial-temporal behavior recognition is one of the most foundational tasks of computer vision. The 2D neural networks of deep learning are built for recognizing pixel-level information such as images with RGB, RGB-D, or optical flow formats, with the current increasingly wide usage of surveillance video and more tasks related to human action recognition. There are increasing tasks requiring temporal information for frames dependency analysis. The researchers have widely studied video-based recognition rather than image-based(pixel-based) only to extract more informative elements from geometry tasks. Our current related research addresses multiple novels proposed research works and compares their advantages and disadvantages between the derived deep learning frameworks rather than machine learning frameworks. The comparison happened between existing frameworks and datasets, which are video format data only. Due to the specific properties of human actions and the increasingly wide usage of deep neural networks, we collected all research works within the last three years between 2020 to 2022. In our article, the performance of deep neural networks surpassed most of the techniques in the feature learning and extraction tasks, especially video action recognition.</p>

show abstract

“…Featured by openness and sharing, open educational resources greatly facilitate the learning and education around the world. Over the years, large public education systems have been formed and spread across the globe [1]. The Chinese Ministry of Education has released a series of policies on teaching quality and reform, calling for the construction of high-quality online open courses.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Teaching Features and Behaviors in Online Open Courses Based on Image Processing

Li¹,

Chai²

2021

View full text Add to dashboard Cite

High-quality online open courses have a wide audience. To further improve the quality of these courses, it is critical to analyze the teaching behaviors in class, which are the manifestation of the overall quality of the teacher. Considering the popularity of image processing-based behavior recognition in many disciplines, this paper explores deep into the teaching features and behaviors in online open courses based on image processing. Firstly, a coding scale was designed for teaching behaviors in online open courses. Next, the principle of optical flow solving was explained for teaching video images. Then, a teaching behavior feature extraction model was established based on dual-flow deep CNN, and used to extract the key points of teacher body and the behavior features of the teacher. After that, a teaching behavior recognition method was developed combining histogram of oriented gradients (HOG) and support vector machine (SVM) to accurately allocate the teaching features and behaviors to the corresponding teaching links. Finally, the proposed model was proved effective through experiments. Based on the recognized teaching behaviors, the frequency and duration of such behaviors were subject to comparative analysis, revealing the teaching features in high-quality online open courses.

show abstract

Survey on Human Behavior Recognition Using Affective Computing

Cited by 12 publications

References 18 publications

Deep Neural Networks in Video Human Action Recognition: A review

Deep Neural Networks in Video Human Action Recognition: A review

Deep Neural Networks in Video Human Action Recognition: A review

Recognition of Teaching Features and Behaviors in Online Open Courses Based on Image Processing

Contact Info

Product

Resources

About